In place of fear: aligning health care planning with system objectives to achieve financial sustainability
Bibliographic record
Abstract
The financial sustainability of publicly funded health care systems is a challenge to policymakers in many countries as health care absorbs an ever increasing share of both national wealth and government spending. New technology, aging populations and increasing public expectations of the health care system are often cited as reasons why health care systems need ever increasing funding as well as reasons why universal and comprehensive public systems are unsustainable. However, increases in health care spending are not usually linked to corresponding increases in need for care within populations. Attempts to promote financial sustainability of systems such as limiting the range of services is covered or the groups of population covered may compromise their political sustainability as some groups are left to seek private cover for some or all services. In this paper, an alternative view of financial sustainability is presented which identifies the failure of planning and management of health care to reflect needs for care in populations and to integrate planning and management functions for health care expenditure, health care services and the health care workforce. We present a Health Care Sustainability Framework based on disaggregating the health care expenditure into separate planning components. Unlike other approaches to planning health care expenditure, this framework explicitly incorporates population health needs as a determinant of health care requirements, and provides a diagnostic tool for understanding the sources of expenditure increase.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".